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Updated: Oct 9, 2025

A Practical Guide to Phylogenetics for Nonexperts

A Practical Guide to Phylogenetics for Nonexperts

Published on: February 5, 2014

Accounting for spatial sampling patterns in Bayesian phylogeography.

Stéphane Guindon¹, Nicola De Maio²

¹Department of Computer Science, Laboratoire d'Informatique, de Robotique et de Microélectronique de Montpellier, CNRS and Université de Montpellier, 34095 Montpellier, France; guindon@lirmm.fr.

Proceedings of the National Academy of Sciences of the United States of America

|December 21, 2021

Summary

This summary is machine-generated.

Statistical phylogeography can be improved by accounting for biased sampling. This study introduces a Bayesian method to correct for non-uniform sample collection in evolutionary analyses.

Keywords:

Bayesian inference West Nile virus phylogeography sampling design statistical modeling

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Area of Science:

Evolutionary biology
Genetics
Ecology

Background:

Statistical phylogeography analyzes organismal spread using georeferenced genetic data.
A common assumption is uniform sampling, which is often violated in real-world scenarios.
Biased sampling can distort inferences of spatial and demographic dynamics.

Purpose of the Study:

To develop a statistical method that explicitly accounts for spatial sampling strategies in phylogeographic analyses.
To address the pervasive issue of non-uniform sample collection in evolutionary studies.
To improve the accuracy of inferring biodiversity patterns across space and time.

Main Methods:

Introduction of a novel Bayesian modeling approach designed for spatial sampling.
Development of an inference technique utilizing recent statistical computing advancements.
Application of the method to georeferenced genetic sequences of West Nile virus in North America and simulated data.

Main Results:

The study demonstrates how spatial sampling strategies can significantly impact phylogeographic inferences.
The proposed Bayesian method effectively accommodates preferential sampling.
Analysis revealed potential biases in understanding evolutionary processes due to sampling assumptions.

Conclusions:

Explicitly modeling spatial sampling strategies is crucial for accurate phylogeographic inference.
The developed Bayesian approach offers a robust solution for handling biased genetic data.
This work enhances our understanding of biodiversity dynamics by correcting for sampling artifacts.